Q1: Inter step by ste Regression

Which of the variables is the indepenent variable and dependent variable for the following question. fit a simple linear regression model to predict latitudes using average monthly range lat= latitudes range= the average monthly range between mean montly maximum and minimum temperatures for a selected set of US cities.

Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 119 7.4 157 9.9 9.3 8.8 8.4 253 202 204 194 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)

Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample…

Listed below are the overhead widths​ (cm) of seals measured from photographs and weights​ (kg) of the seals. Find the regression​ equation, letting the overhead width be the predictor​ (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8​cm, using the regression equation. Can the prediction be​ correct? If​ not, what is​ wrong? Use a significance level of 0.05.   Overhead Width​ (cm) 7.1 7.3 9.9 9.3 8.8 8.3   Weight​ (kg) 137 176 282 230 230 214         The regression equation is y=+x. ​(Round the constant to the nearest integers needed. Round the coefficient to one decimal place as​ needed.) The best-predicted weight for an overhead width of 1.8 ​cm, based on the regression​ equation, is: ____ kg.   ​(Round to one decimal place as​ needed.)   Can the prediction be​ correct? If​ not, what is​ wrong?     A. The prediction cannot be correct because a weight of zero does not…

Selling price and percent of advertising budget spent were into mutiple regression to determine what affects flat panel LCD TV sales. The regression coefficient for Price was found to be -0.03055, which of the correct interpretation for this value? Increasing the price of Sony Bravia by $100 will result in at least 3 fewer TV's sold. For a given percent of advertising budget spent, a $100 increase in price of Sony Bravia is associated with a dercrease in sales of 3.055 units, on average. After following for the percent of advertising budget spent on advertising, an increase of $100 in the price of Sony Bravia will decrease in sales by 3.055 units. Holding the percent of advertising budget spent constant , an increase of $100 in the price of the Sony Bravia will decrease sales by 0.03%. None of the above.

QF3

See the attached image for the introduction.  What are two-sided 95% confidence limits for the increase in mean selling price associated with each additional square foot? (Plugin completely, but you need not simplify.)

Why is the equation of the regression line for this scatter plot ?